Dataset Information

The perceptual processing capacity of summary statistics between and within feature dimensions.

ABSTRACT: The simultaneous-sequential method was used to test the processing capacity of statistical summary representations both within and between feature dimensions. Sixteen gratings varied with respect to their size and orientation. In Experiment 1, the gratings were equally divided into four separate smaller sets, one of which with a mean size that was larger or smaller than the other three sets, and one of which with a mean orientation that was tilted more leftward or rightward. The task was to report the mean size and orientation of the oddball sets. This therefore required four summary representations for size and another four for orientation. The sets were presented at the same time in the simultaneous condition or across two temporal frames in the sequential condition. Experiment 1 showed evidence of a sequential advantage, suggesting that the system may be limited with respect to establishing multiple within-feature summaries. Experiment 2 eliminates the possibility that some aspect of the task, other than averaging, was contributing to this observed limitation. In Experiment 3, the same 16 gratings appeared as one large superset, and therefore the task only required one summary representation for size and another one for orientation. Equal simultaneous-sequential performance indicated that between-feature summaries are capacity free. These findings challenge the view that within-feature summaries drive a global sense of visual continuity across areas of the peripheral visual field, and suggest a shift in focus to seeking an understanding of how between-feature summaries in one area of the environment control behavior.

SUBMITTER: Attarha M

PROVIDER: S-EPMC4679208 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The perceptual processing capacity of summary statistics between and within feature dimensions.

Attarha Mouna M Moore Cathleen M CM

Journal of vision 20150101 4

The simultaneous-sequential method was used to test the processing capacity of statistical summary representations both within and between feature dimensions. Sixteen gratings varied with respect to their size and orientation. In Experiment 1, the gratings were equally divided into four separate smaller sets, one of which with a mean size that was larger or smaller than the other three sets, and one of which with a mean orientation that was tilted more leftward or rightward. The task was to repo ...[more]

PMID: 26360153

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Project description:Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

Project description:BackgroundColocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics.ResultsWe evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets.ConclusionsWe find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

Dataset Information

The perceptual processing capacity of summary statistics between and within feature dimensions.

Publications

The perceptual processing capacity of summary statistics between and within feature dimensions.

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